In this section, a comprehensive set of experiments is conducted to evaluate the performance of the proposed \(\text{HDCAM}\)  architecture. Table REF  represents the sequence of the \(\text{\textit {HD}Conv}\)  and \(\text{\textit {MHS}Atten}\)  encoders along with design information of the extra-extra small (XXSmall), extra-small (XSmall), and small (Small) versions of the model. As shown in Table REF , the type, number, and sequence of the component blocks in the overall model architecture (illustrated in Fig. REF ) are maintained across all \(\text{HDCAM}\)  architecture variants. The number of output channels in each stage is what distinguishes the \(\text{XXSmall}\) , \(\text{XSmall}\) , and \(\text{Small}\)  models from one another. Since the number of active branches (\(s\) ) and attention heads (\(h\) ) in the \(\text{\textit {HD}Conv}\)  and \(\text{\textit {MHS}Atten}\)  encoders is proportional to the number of output channels of the corresponding stage, we maintained the fundamental rule for all model variants, which requires having at least eight channels per-head/per-branch. Additionally, the maximum allowed number of heads/branches is set to four. All models were trained using the Adam optimizer at a learning rate of \(0.0001\)  with Cross-Entropy loss. Furthermore, we chose a mini-batch size of 32.
For the pre-processing step, we employed the 1st order 1 Hz low-pass Butterworth filter to smooth raw sEMG signals as described in the recent literature [1]}, [2]}, [3]}, [4]}. Moreover, we normalized and scaled the sEMG signals by the \(\mu \) -law technique [5]} with the \(\mu \) value of 256.
Table REF  shows the average recognition accuracy of different variants over all subjects. In the following sections, we focus on five experiments to evaluate our proposed
\(\text{HDCAM}\)  model:
